Novel Therapies to Reduce Rehospitalization Risk in Worsening Heart Failure
Bibliographic record
Abstract
Background: Worsening heart failure (WHF) challenges health care with frequent rehospitalizations and reduced quality of life for patients. Despite therapeutic advances, high rehospitalization risks highlight the urgent need for new treatments. Objectives: This study evaluated the effectiveness of initiating novel therapies during hospitalization or vulnerable phase for WHF patients to reduce rehospitalization risks and determine the optimal treatment sequence. Methods: A systematic review and network meta-analysis were performed in accordance with Cochrane Collaboration and Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. We included randomized clinical trials from January 2013, to December 2022, sourced from PUBMED and EMBASE, comparing novel heart failure therapies against control. The primary outcome was heart failure rehospitalization. Results: = 0.016). These therapies, when initiated during hospitalization, markedly influenced rehospitalization outcomes. Conclusions: Early administration of sodium-glucose co-transporter 2 inhibitors, ARNI, and ferric carboxymaltose for WHF patients significantly reduces rehospitalization risk. Our findings support a strategic shift in WHF management, advocating for the rapid introduction of these novel therapies to enhance patient prognosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".